Basic R Easy

7/28/2019 Basic R Easy

1/113

Outline

UCLA Department of Statistics

Statistical Consulting Center

Basic RBrigid Wilson

[email protected]

April 5, 2010

Brigid Wilson [email protected]

Basic R UCLA SCC
http://find/http://goback/


2/113

Outline

Outline

I. Preliminaries

II. Variable Assignment

III. Working with Vectors

IV. Working with Matrices

V. From Vectors to Matrices

VI. More on Handling Missing Data

VII. The Help System

VIII. Datasets in R

IX. Overview of Plots

X. R Environment

XI. Common Bugs and Fixes

XII. Online Resources for R

XIII. Exercises

XIV. Upcoming Mini-Courses


Basic R UCLA SCC
http://find/


3/113

Preliminaries

Part I

Preliminaries


Basic R UCLA SCC
http://find/


4/113

Preliminaries

Software Installation

Installing R on Mac

1 Go tohttp://cran.r-project.org

and select MacOS X.2 Select to download the

latest version: 2.10.1

3 Install and Open. The R

window should look likethis:


Basic R UCLA SCC


5/113

Preliminaries

Software Installation

Installing R on Windows

1 Go tohttp://cran.r-project.org

and select Windows.

2 Select base to install theR system.

3 Click on the largedownload link. There isother information

available on the page.4 Install and Open. The R

window should look likethis:


Basic R UCLA SCC

V bl A
http://find/


6/113

Variable Assignment

Part II

Variable Assignment


Basic R UCLA SCC

V i bl A i
http://find/


7/113

Variable Assignment

Creating Variables

Creating Variables I

To use R as a calculator, type an equation and hitENTER. (Note how R prints the result.) Your outputshould look like this:

1 2 + 52 [1] 7


Basic R UCLA SCC

V i ble Assi e t
http://find/


8/113

Variable Assignment

Creating Variables

Creating Variables II

To create variables in R, use either


9/113

Variable Assignment

Creating Variables

Creating Variables III

Caution!Be careful when using


10/113

Variable Assignment

Creating Variables

Creating Variables IV

Use spaces so that R will not be confused. It is better to useparentheses instead.

1 a


11/113

Variable Assignment

Creating Variables

Creating Variables V

Caution!It is important not to name your variables after existingvariables or functions. For example, a bad habit is to nameyour data frames data. data is a function used to load some

datasets.If you give a variable the same name as an existing

constant, that constant is overwritten with the value of

the variable. So, it is possible to define a new value for .


Basic R UCLA SCC

Variable Assignment
http://find/


12/113

Variable Assignment

Creating Variables

Creating Variables VI

Caution!On the other hand, if you give a variable the same name as anexisting function, R will treat the identifier as a variable if usedas a variable, and will treat it as a function when it is used as

a function:

c


13/113

g

Creating Variables

Creating Variables VII

Caution!

As we have seen, you can get away with using the same namefor a variable as with an existing function, but you will be introuble if you give a name to a function and a function withthat name already exists.


Basic R UCLA SCC

Working with Vectors
http://find/


14/113

Part III



Basic R UCLA SCC

http://find/


15/113

Creating Vectors

Creating Vectors I

Scalars are the most basic vectors. To create vectors of lengthgreater than one, use the concatenation function c():

1 d = c (3 , 4 ,7) ; d

2

[1] 3 4 7

The More You Know...

The semicolon ; is used to combine multiple statements on

one line.


Basic R UCLA SCC



16/113

Creating Vectors

Creating Vectors II

To create a null vector:

1 x =c () ; x

2 NULL


Basic R UCLA SCC



17/113

Creating Vectors

Creating Vectors III

Creating a vector with equal spacing, use the sequencefunction seq():

1 e = seq ( f rom = 1 , to =3 , by = 0 .5 ) ; e

2 [1] 1.0 1.5 2.0 2.5 3.0

Creating a vector of a given length, use the repeat functionrep():

1 f = rep ( NA , 6) ; f

2 [1] NA NA NA NA NA NA


Basic R UCLA SCC



18/113

Some Vector Functions

Some Useful Vector Functions I

To find the length of the vector, use length():

1 l e n g t h ( d )

2 [1] 3

To find the maximum value of the vector, use the maximumfunction max():

1 m a x ( d )

2 [1] 7


Basic R UCLA SCC

http://find/


19/113


Some Useful Vector Functions II

To find the minimum value of the vector, use the minimumfunction min():

1 m i n ( d )

2 [1] 3

To find the mean of the vector, use mean():

1 m e a n ( d )

2 [ 1] 4 . 66 6 66 7


Basic R UCLA SCC



20/113


Some Useful Vector Functions III

To sort the vector, use sort():

1 g < - c ( 2 , 6 , 7 , 4 , 5 , 2 , 9 , 3 , 6 , 4 , 3 )

2 s or t ( g , d e cr e a si n g = T R U E )

3 [1] 9 7 6 6 5 4 4 3 3 2 2

Caution!

Although T and F work in place of TRUE and FALSE, it is notrecommended.


Basic R UCLA SCC

http://find/


21/113


Some Useful Vector Functions IV

To find the unique elements of the vector, use unique():

1 u n i q u e ( g )

2 [1] 2 6 7 4 5 9 3

Alternatively, to find the elements of the vector that repeat,use duplicated():

1 d u p l i c a t e d ( g )

2 [1] FALSE FALSE FALSE FALSE FALSE TRUE3 [7] FALSE FALSE TRUE TRUE TRUE

Brigid Wilson [email protected] R UCLA SCC



22/113


Some Useful Vector Functions V

To determine if a value is missing (NA), use is.na. This isuseful for finding missing values and removing them, or doingsomething else with them.

1 a


23/113


Some Useful Vector Functions VI

Caution!mean(a)

[1] NA

mean(a, na.rm=TRUE)

[1] 1.5


http://find/


24/113


Some Useful Vector Functions VII

To get the number of missing values in a vector,

1 s u m ( i s . n a ( a ) )

2 [1] 1

There are other ways to handle missing values. See?na.action.


http://find/


25/113


Some Useful Vector Functions VIII

One final common function you can use on vectors (and otherobjects) is summary.

1 s u m m a r y ( a )

Min. 1st Qu. Median Mean 3rd Qu. Max.

1.00 1.75 2.50 3.00 3.75 6.00

NAs

1.00

There are many, many other functions you can use on vectors!


http://find/


26/113

Comparisons in R

Comparisons in R

Symbol Meaning! logical NOT

& logical AND logical OR< less than greater than>= greater than or equal to

== logical equals! = not equal




27/113

Subsetting with Vectors

Subsetting with Vectors I

To find out what is stored in a given element of the vector,use [ ]:

1

d [ 2 ]2 [1] 4

To see if the elements of a vector equal a certain number,use ==:

1 d = = 32 [1] T RUE F AL SE F AL SE



S b i i h V
http://find/


28/113


Subsetting with Vectors II

To see if any of the elements of a vector do not equal acertain number, use !=:

1 d ! = 3

2 [1] FALSE TRUE TRUE



S b tti ith V t


29/113


Subsetting with Vectors III

To obtain the element number of the vector when a conditionis satisfied, use which():

1 w h i c h ( d = = 4 )

2 [1] 2

To store the result, type: a=which(d==4); a





30/113


Subsetting with Vectors IV

We can also tell R what we do not want when subsetting byusing the minus - sign. To obtain everything but the 2ndelement,

1 d


31/113


Subsetting with Vectors V

We can use subsetting to explicitly tell R what observations wewant to use. To get all elements of d greater than or equal to2,

1 d [ d >= 2]2 [1] 3 5 7 9

R will return values of d where the expression within brackets

is TRUE. Think of these statements as: give me all d suchthat d 2.





32/113


Exercise 1

Create a vector of the positive odd integers less than 100Remove the values greater than 60 and less than 80

Find the variance of the remaining set of values


Working with Matrices
http://find/


33/113

Part IV




Creating Matrices
http://find/


34/113

g

Creating Matrices I

To create a matrix, use the matrix() function:

1

m at < - ma tr ix ( 10 :1 5 , n ro w =3 , n co l = 2) ; m at2 [ , 1] [ , 2]

3 [1 ,] 10 13

4 [2 ,] 11 14

5 [3 ,] 12 15



Some Matrix Functions
http://find/


35/113

Some Useful Matrix Functions I

To add two matrices, use +

1 m a t + m a t

2 [ , 1] [ , 2]

3 [1 ,] 20 26

4 [2 ,] 22 28

5 [3 ,] 24 30



http://find/


36/113

Some Useful Matrix Functions II

To find the transpose of a matrix, use t():

1 t ( m a t )2 [ ,1] [ ,2] [ ,3]

3 [1 ,] 10 11 12

4 [2 ,] 13 14 15



http://find/


37/113

Some Useful Matrix Functions III

To find the dimensions of a matrix, use dim():

1 d i m ( m a t )

2 [1] 3 2

Alternatively, we can find the rows and columns of the matrix,

by nrow() and ncol().



http://find/


38/113

Some Useful Matrix Functions IV

To multiply two matrices, use %*%.Note: If you use * instead, you will be performing matrixmultiplication element-wise.

1 m a t % * % t ( m a t )

2 [ ,1] [ ,2] [ ,3]

3 [1 ,] 269 292 315

4 [2 ,] 292 317 342

5 [3 ,] 315 342 369



Subsetting with Matrices
http://find/


39/113

Subsetting with Matrices I

To see what is stored in the first element of the matrix,use [ ]:

1 m a t [ 1 , 1 ]

2 [1] 10

To see what is stored in the first row of the matrix:

1 m a t [ 1 , ]

2 [1] 10 13


Basic R UCLA SCC


http://find/


40/113

Subsetting with Matrices II

To see what is stored in the second column of the matrix:1 mat [ , 2]

2 [1] 13 14 15


Basic R UCLA SCC


http://find/


41/113

Subsetting with Matrices III

To extract elements 1 and 3 from the second column, use c()

and [ ]:1 m at [ c ( 1 , 3) , 2]

2 [1] 13 15


Basic R UCLA SCC

From Vectors to Matrices
http://find/


42/113

Part V



Basic R UCLA SCC


Creating Matrices from Vectors
http://find/


43/113

Creating Matrices from Vectors I

To stack two vectors, one below the other, use rbind():

1 m at 1 < - r b i nd ( d , d ) ; m at 1

2 [ ,1] [ ,2] [ ,3]

3 d 3 4 7

4 d 3 4 7


Basic R UCLA SCC


Creating Matrices from Vectors
http://find/


44/113

Creating Matrices from Vectors II

To stack two vectors, one next to the other, use cbind():

1 m at 2 < - c b i nd ( d , d ) ; m at 2

2 d d3 [1 ,] 3 3

4 [2 ,] 4 4

5 [3 ,] 7 7


Basic R UCLA SCC

More Handling Missing Data
http://find/


45/113

Part VI

More on Handling Missing Data


Basic R UCLA SCC


Missing Data in Matrices


46/113

Missing Data in Matrices I

Start by creating a matrix with missing data:

1 h = m a t ri x ( c ( NA ,3 , 1 ,7 , -8 , NA ) , n ro w = 3 , n c ol

=2 , b yr ow = T RUE ) ; h

2 [ , 1] [ , 2]

3 [1 ,] NA 34 [2 ,] 1 7

5 [3 ,] -8 NA


Basic R UCLA SCC


http://find/


47/113

Missing Data in Matrices II

To see if any of the elements of a vector are missing useis.na():

1 i s . n a ( h )

2 [ ,1] [ ,2]

3 [1 ,] TRUE F ALSE4 [2 ,] F AL SE F AL SE

5 [3 ,] F A LSE TRUE


Basic R UCLA SCC


http://find/


48/113

Missing Data in Matrices III

To see how many missing values there are, use sum() andis.na() (TRUE=1, FALSE=0):

1 s u m ( i s . n a ( h ) )

2 [1] 2

To obtain the element number of the matrix of the missingvalue(s), use which() and is.na():

1 w h i c h ( i s . n a ( h ) )

2 [1] 1 6


Basic R UCLA SCC


http://find/


49/113

Missing Data in Matrices IV

To keep only the rows without missing value(s), usena.omit()

1 n a . o m i t ( h )

2 [ , 1] [ , 2]3 [1 ,] 1 7

4 a t t r ( ," n a . a c t i o n " )

5 [1] 1 3

6 a t t r ( ," c l a s s ")

7 [1] " o m i t "


Basic R UCLA SCC


http://find/


50/113

Exercise 2

Find the matrix product of A and B if

Matrix A=

2 3 71 6 23 5 1

Matrix B=

3 2 90 7 85 8 2


Basic R UCLA SCC

Getting Help in R


51/113

Part VII

The Help System


Basic R UCLA SCC

Getting Help in R

Help with a Function
http://find/


52/113

Help with a Function I

To get help with a function in R, use ? followed by the nameof the function.

1 ? r e a d . t a b l e

help(function name) also works.


Basic R UCLA SCC

Getting Help in R

Help with a Function
http://find/


53/113

Help with a Function II


Basic R UCLA SCC

Getting Help in R

Help with a Package
http://find/


54/113

Help with a Package I

To get help with a package, use help(package="name").

1 h e l p ( p a c k a g e = " M A S S " )


Basic R UCLA SCC

Getting Help in R

Help with a Package

H l i h P k II
http://find/


55/113

Help with a Package II


Basic R UCLA SCC

Getting Help in R

Searching for Help

S hi f H l I
http://find/


56/113

Searching for Help I

To search R packages for help with a topic, usehelp.search().

1 h e l p . s e a r c h ( " r e g r e s s i o n " )


Basic R UCLA SCC

Getting Help in R

Searching for Help

S hi f H l II
http://find/


57/113

Searching for Help II


Basic R UCLA SCC

Datasets in R
http://find/


58/113

Part VIII

Datasets in R


Basic R UCLA SCC

Datasets in R

Importing Datasets into R

D t f th I t t I
http://find/


59/113

Data from the Internet I

When downloading data from the internet, useread.table(). In the arguments of the function:

header if TRUE, tells R to include variables names whenimporting

sep tells R how the entires in the data set are separatedsep="," when entries are separated by COMMASsep="\t" when entries are separated by TABsep=" " when entries are separated by SPACE


Basic R UCLA SCC

Datasets in R


D t f th I t t II
http://find/


60/113

Data from the Internet II

1 s t o c k . d a t a < - r e a d . t a b l e ( " h t t p : / / w w w . g o o g l e

. c o m / f i n a n c e / h i s t o r i c a l ? q = N A S D A Q : A A P L &o u t p u t = c s v " , h ea de r = TRUE , sep = " ," )


Basic R UCLA SCC

Datasets in R


Importing Data from Your Computer I


61/113

Importing Data from Your Computer I

1 Check what folder R is working with now:

1 g e t w d ( )

2 Tell R in what folder the data set is stored (if differentfrom (1)). Suppose your data set is on your desktop:

1 s e t w d (" ~ / D e s k t o p ")

3

Now use the read.table() command to read in thedata, substituting the name of the file for the website.


Basic R UCLA SCC

Datasets in R


Using Data Available in R I
http://find/


62/113

Using Data Available in R I

To use a data set available in one of the R packages, installthat package (if needed). Load the package into R, using thelibrary() function.

1 l i b r a r y ( a l r 3 )

Extract the data set you want from that package, using thedata() function. In our case, the data set is called UN2.

1 d a t a ( U N 2 )


Basic R UCLA SCC

Datasets in R

Working with Datasets in R

Working with Datasets in R I
http://find/


63/113

Working with Datasets in R I

To use the variable names when working with data, use

attach():1 d a t a ( U N 2 )

2 a t t a c h ( U N 2 )


Basic R UCLA SCC

Datasets in R


Working with Datasets in R II
http://find/


64/113

Working with Datasets in R II

After the variable names have been attached, to see thevariable names, use names():

1 n a m e s ( U N 2 )

To see the descriptions of the variables, use ?:

1 ? U N 2


Basic R UCLA SCC

Datasets in R


Working with Datasets in R III
http://find/


65/113

Working with Datasets in R III

After modifying variables, use detach() and attach() tosave the results:

1 # Ma ke a co py of the da t a set

2 U N 2 . c o p y < - U N 2

3 d e t a c h ( U N 2 )

4 a t t a c h ( U N 2 . c o p y )

5 # C h a ng e the 10 th o bs er va ti on for

l o g F e r t i l i t y

6 U N2 . c op y [ 10 , 2 ] < - 9 99


Basic R UCLA SCC

Datasets in R


Working with Datasets in R IV
http://find/


66/113

Working with Datasets in R IV

To get an overview of the data sets and its variables, use thesummary() function:

1 # C h e c k t ha t the c h a n g e has be e n m ad e

2 s u m m a r y ( U N 2 )

3 s u m m a r y ( U N 2 . c o p y )


Basic R UCLA SCC

Datasets in R


Working with Datasets in R V
http://find/


67/113

Working with Datasets in R V

Caution!

Avoid using attach() if possible. Many strange things canoccur if you accidentally attach the same data frame multipletimes, or forget to detach. Instead, you can refer to a variableusing $. To access the Locality variable in data frame UN2,

use UN2$Locality. You can also get around this by using thewith function or if your function of choice takes dataargument.

attach at your own risk!


Basic R UCLA SCC

Datasets in R


Working with Datasets in R VI
http://find/


68/113

Working with Datasets in R VI

To get the mean of all the variables in the data set, usemean():

1 m e a n ( U N 2 , n a . r m = T R U E )

2 l og P P g d p l o g F e r t i l i t y P urb an

3 1 0. 99 30 94 1 . 0 1 8 01 6 5 5 . 5 3 8 8 6 0


Basic R UCLA SCC

Datasets in R


Working with Datasets in R VII
http://find/


69/113

Working with Datasets in R VII

To get the correlation matrix of all the (numerical) variables inthe data set, use cor():

1 c o r ( U N 2 [ , 1 : 2 ] )

2 l o gP P gd p l o g Fe r t il i t y3 l o g PP g d p 1 . 0 00 0 0 0 -0.677604

4 l o g F e r t i l i t y -0.677604 1. 0 0 0 0 0 0

Can similarly obtain the variance-covariance matrix using

var().


Basic R UCLA SCC

Datasets in R


Exercise 3
http://find/


70/113

Exercise 3

Load the Animals dataset from the MASS package

Examine the documentation for this dataset

Find the correlation coefficient of brain weight and bodyweight in this dataset


Basic R UCLA SCC

Overview of Plots in R
http://find/


71/113

Part IX



Basic R UCLA SCC


Creating Plots

Basic scatterplot I
http://find/


72/113

p

To make a plot in R, you can use plot():

1 p l o t ( x = U N 2 $ l o g P Pg d p ,

2 y = U N2 $ l o gF e rt i li ty ,

3 main = " F e rt il it y vs . P er Ca pi ta GDP " ,

4 xlab = " log P er Ca pi ta GDP , in $ US " ,

5 ylab = " L og F er ti li ty " )


Basic R UCLA SCC


Creating Plots

Basic scatterplot II
http://find/


73/113

p

8 10 12 14

0.

0

0.

5

1.

0

1.5

2.

0

Fertility vs. PerCapita GDP

log PerCapita GDP, in $US

Log

Fertili

ty


Basic R UCLA SCC


Creating Plots

Histogram I
http://find/


74/113

g

To make a histogram in R, you can use hist():

1 h i s t ( U N 2 $ l o g PP g d p ,2 main = " D i st ri bu ti on of P er Ca pi ta GDP " ,

3 xlab = " l og P er Ca pi ta GDP " )


Basic R UCLA SCC


Creating Plots

Histogram II
http://find/


75/113

g

Distribution of PerCapita GDP

log PerCapita GDP

Frequenc

y

6 8 10 12 14 16

0

5

10

15

20

25

30


Basic R UCLA SCC


Creating Plots

Boxplot I
http://find/


76/113

p

To make a boxplot in R, you can use boxplot():

1 b o x p l o t ( U N 2 $ l o g PP g d p ,

2 main = " B o xp lo t of P er Ca pi ta GDP " )


Basic R UCLA SCC


Creating Plots

Boxplot II


77/113

8

10

12

14

Boxplot of PerCapita GDP


Basic R UCLA SCC


Creating Plots

Matrix of Scatterplots I
http://find/


78/113

To make scatterplots of all the numeric variables in your

dataset in R, you can use pairs():1 p a i r s ( U N 2 )


Basic R UCLA SCC


Creating Plots

Matrix of Scatterplots II
http://find/


79/113

logPPgdp

0.0 0.5 1.0 1.5 2.0

8

10

12

14

0.

0

0.

5

1.

0

1.

5

2.

0

logFertility

8 10 12 14 20 40 60 80 100

20

40

60

80

100

Purban


Basic R UCLA SCC


Creating Plots

Overlaying with points I
http://find/


80/113

To add more points to an existing plot, use points().Here,we will first plot fertility vs. PerCapita GDP first where %urban is less than 50.

1 a t t a c h ( U N 2 )

2 p lo t ( l o gP Pg dp [ P ur ba n < 50] ,

3 l og F er t il i ty [ P ur ba n < 50] ,

4 main = " F er ti li ty vs . PPGDP , by % Urban " ,

5 xlab = " l og P PG DP " ,6 ylab = " l og F er ti li ty " )


Basic R UCLA SCC


Creating Plots

Overlaying with points II


81/113

We then add in the points where % urban is more than 50 andmark these points with a different color.

1 p oi nt s ( l o g PP gd p [ P u rb an >= 50] ,

2 l og F er t il i ty [ P ur ba n >= 50] ,

3 col = " r e d ")


Basic R UCLA SCC


Creating Plots

Overlaying with points III
http://find/


82/113

8 10 12 14

0.

5

1.

0

1.

5

2.

0

Fertility vs. PPGDP, by % Urban

log PPGDP

log

Fertility


Basic R UCLA SCC


Creating Plots

Overlaying
http://find/


83/113

Caution!

Once a plot is constructed using plot, whatever is containedin the plot cannot be modified. To overlay things on a

rendered plot, use one of the following1 abline - add a line with slope b, intercept a or

horizontal/vertical.

2 points - add points.

3 lines - add lines.


Basic R UCLA SCC Overview of Plots in R

Saving Plots as a PDF

Saving Plots as a PDF I
http://find/


84/113

Note: The files will be saved in the folder specified withsetwd().

To save a plot in R as a PDF, you can use pdf():1 p d f ( " m y p l o t . p d f " )

2 p a i r s ( U N 2 )

3 d e v . o f f ( )


Basic R UCLA SCC R Environment Exporting R Objects to Other Formats
http://find/


85/113

Part X

R Environment



Exploring R Objects

Exploring R Objects I
http://find/


86/113

To see the names of the objects available to be saved (in yourcurrent workspace), use ls().

1 l s ( )

[1] "UN2" "a" "b" "d" "data" "e" "f" "h" "mat1" "mat2"



Exploring R Objects

Exploring R Objects II
http://find/


87/113

To remove objects from your workspace, use rm().

1 r m ( d )

2 l s ( )

[1] "UN2" "a" "b" "data" "e" "f" "h" "mat1" "mat2"



Exploring R Objects

Exploring R Objects III
http://find/


88/113

To remove all the objects from your workspace, type:

1 r m ( l i s t = l s ( ) )

2 l s ( )

character(0)



Saving and Loading R Objects

Saving and Loading R Objects I
http://find/


89/113

To save (to the current directory) all the objects in theworkspace, use save.image().

1 s a v e . i m a g e ( " b a s i c R . R D a t a " )

To load (from the current directory), use load().

1 l o a d ( " b a s i c R . R D a t a " )



Saving and Loading R Objects

Saving and Loading R Objects I
http://find/


90/113

To save (to the current directory) a single object in theworkspace, use save().

1 s a v e ( s t o c k . d a t a , f i l e = " s t o c k s . R D a t a " )

To load (from the current directory), use load().

1 l o a d ( " s t o c k s . R D a t a " )



Exporting R Objects to Other Formats I
http://find/


91/113

To save (to the current directory) certain objects in theworkspace to be used in Excel, use write.csv().

1 w r i t e . c s v ( s t o c k . d a t a ,

2 f i l e = " s t o c k d a t a . c s v " )



Saving R Commands

Saving R Commands I
http://find/


92/113

To see all of the commands you typed in an R session,click on the Yellow and Green Tablet



Saving R Commands

Saving R Commands II

T ll f th d t d i R i
http://find/


93/113

To save all of the commands you typed in an R session,

use:1 s a v e h i s t o r y ( f i l e = " h i s t o r y . l o g " )



Saving R Commands

Saving R Commands III
http://find/


94/113

Alternatively, use a .r file to store your commands.1 Go to: File -> New Document2 Type your commands3 Save the file as "code.r"4 Go back to the R Console5 To run all the commands, use:

1 s o u r c e (" c o d e . r " )

The More You Know...

Use the # sign to write comments in your code. Use them!


Basic R UCLA SCC Common Bugs and Fixes
http://find/


95/113

Part XI

Common Bugs and Fixes


Basic R UCLA SCC Common Bugs and Fixes

Syntax Error

Error: syntax error


96/113

Possible causes:

Incorrect spelling (of the function, variable, etc.)

Including a + when copying code from the ConsoleHaving an extra parenthesis at the end of a function

Having an extra bracket when subsetting


Basic R UCLA SCC
http://find/


97/113

Common Bugs and Fixes

Error When Performing Operations

Error in ... : requires numeric

matrix/vector arguments


98/113

matrix/vector arguments

Possible causes:

1 Objects are data frames, not matrices

2

Elements of the vectors are charactersPossible solutions:

1 Coerce (a copy of) the data set to be a matrix, with theas.matrix() command

2 Coerce (a copy of) the vector to have numeric entries,with the as.numeric() command


Basic R UCLA SCC Useful Links for R
http://find/


99/113

Part XII

Online Resources for R


Basic R UCLA SCC

Useful Links for R

CRAN
http://find/


100/113

http://cran.stat.ucla.edu/


Basic R UCLA SCC

Useful Links for R

R-Seek Search Engine
http://find/


101/113

http://www.rseek.org


Basic R UCLA SCC

Useful Links for R

UCLA Statistics Bootcamp Resources
http://find/


102/113

R Bootcamp is a day-long introduction to R. Handouts and

datasets from Bootcamp 2008 can be found on Ryan Rosarioswebsite: http://www.stat.ucla.edu/rosario/boot08/


Basic R UCLA SCC

Useful Links for R

UCLA Statistics Information Portal
http://find/


103/113

http://info.stat.ucla.edu/grad/


Basic R UCLA SCC

Useful Links for R

UCLA Statistical Consulting Center E-consulting

and Walk-in Consulting
http://find/


104/113

g

http://scc.stat.ucla.edu


Basic R UCLA SCC

Exercises
http://find/


105/113

Part XIII

Exercises


Basic R UCLA SCC

Exercises

Exercise 1
http://find/


106/113

Create a vector of the positive odd integers less than 100

Remove the values greater than 60 and less than 80

Find the variance of the remaining set of values


Basic R UCLA SCC

Exercises

Exercise 2
http://find/


107/113

Find the matrix product of A and B if

Matrix A=

2 3 71 6 23 5 1

Matrix B=

3 2 90 7 85 8 2


Basic R UCLA SCC

Exercises

Exercise 3
http://find/


108/113

Load the Animals dataset from the MASS package

Examine the documentation for this datasetFind the correlation coefficient of brain weight and bodyweight in this dataset


Basic R UCLA SCC

Exercises

Solutions I
http://find/


109/113

1 e1


110/113

1 A


111/113

1 l i b r a r y ( M A S S )

2 d a t a ( A n i m a l s )

3 ? A n i m a l s

4 c o r ( A n i m a l s )5 body bra in

6 body 1 .0 00 00 00 00 -0 .00 53 411 63

7 b ra in - 0. 00 53 41 16 3 1 .0 00 00 00 00


Basic R UCLA SCC
http://find/


112/113

Upcoming Mini-Courses


113/113

April 7 - R Programming II: Data Manipulation and Functions

April 12 - LaTex I: Writing a Document, Paper, or Thesis

April 14 - LaTeX II: Bibliographies, Style and Math in LaTeXApril 19 - LaTeX III: Sweave, Embedding R in LaTeX

For a schedule of all mini-courses offered please visithttp://scc.stat.ucla.edu/mini-courses.


Basic R UCLA SCC
http://find/

Documents

Basic R Easy